A new offline reinforcement learning method named OREO (Offline REasoning Optimization) has been introduced to enhance multi-step reasoning capabilities in large language models (LLMs). This innovative approach aims to address the limitations of existing methods by improving the efficiency and effectiveness of reasoning processes. Additionally, other recent advancements in LLM technology include techniques for byte-level processing, which can reduce computational power usage by 50%, and the introduction of Compressed Chain-of-Thought (CCoT) decoding, which allows LLMs to reason more quickly using shorter, denser reasoning tokens. Furthermore, a new compression approach tailored for long context LLM retrieval has shown to improve retrieval performance by 6% while reducing input size by 1.91 times, enhancing the overall efficiency of LLMs in handling long contexts.